基于XDense-RC-net的CXR图像分类算法

Improved CXR image classification algorithm based on XDense-RC-net

卷积神经网络逐渐应用于胸部X射线(chset X-ray,CXR)图像分类领域,目前普遍使用迁移学习技术进行分类研究,但在快速构建网络时未能考虑CXR图像的特异性。针对上述问题,提出了一种新型的XDense-RC-net方法。该方法对DenseNet模型进行改进,在原密集连接层引入新提出的空间注意力机制,实现特征提取和特征融合,优化DenseNet的transition模块,同时使用两种不同的池化策略增强模型的抗扰动能力。实验使用chest X-ray14多标签14分类数据集和COVIDx单标签3分类数据集对XDense-RC-net进行验证。在多标签分类实验中,平均AUC值达到0.854,比基准方法提升了0.109。在单标签分类实验中,平均准确率达到96.75%,相较于基准方法提升了7.75%。结果显示,XDense-RC-net提升了CXR图像分类的精度,并能够泛化至多标签和单标签两种不同的分类任务中。

In the field of CXR image classification,current studies mostly use convolutional neural network and transfer lear-ning technology.But in the process of rapidly constructing a network without considering the specificity of the CXR image.To solve the above problems,this paper proposed a novel XDense-RC-net.Based on the DenseNet model,this method improved the capability of feature extraction and feature fusion by introducing a new spatial attention mechanism in the densely connected layer,optimized transition module of DenseNet by using two different pooling strategies to enhance the noise immunity of the model.XDense-RC-net used the chest X-ray14(multi-label and 14-class)and COVIDx(single label and 3-class)datasets for validating.In the multi-label classification experiments,the average AUC score reaches 0.854,which is 0.109 higher than the benchmark method.In the single-label classification experiments,the average accuracy reaches 96.75%,which is 7.75%higher than the baseline model.The results show that XDense-RC-net improves the accuracy of CXR image classification and can generalize to multi-label and single-label classification tasks.